Systems and methods for remote pilot control of an electric aircraft

ABSTRACT

A system for remote pilot control of an electric aircraft in autopilot mode including a remote computing device configured to receive a user input and generate a control datum as a function of the pilot input, a flight controller configured to receive the control datum from the remote computing device, and generate a command datum as a function of the control datum and an authority status, and the remote computing device configured to receive the command datum from the flight controller, and display the command datum.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to systemsand methods for remote pilot control of an electric aircraft duringautopilot mode operation.

BACKGROUND

In an electric aircraft, it desirable to have an autopilot system thatprevents a user from, or warn against, performing certain actions thatmay put the aircraft and the user at risk, such as pushing the aircraftbeyond a safe pitch level, or actions that may be against certainregulations, such as going above a specific speed in a restrictedairspace, such as close to buildings in a city or flying near anairport.

SUMMARY OF THE DISCLOSURE

In an aspect a system for remote pilot control of an electric aircraftduring autopilot, the system including: a flight controllercommunicatively connected to the remote computing device and an electricaircraft, wherein the flight controller is configured to: receive acontrol datum from an input control; determine an authority status ofthe control datum; and generate a command datum as a function of thecontrol datum and the authority status, wherein the authority statuscomprises the amount of control a user has over the electric aircraftwhile the electric aircraft is engaged in an autopilot mode.

In another aspect a method for autopilot in an electric aircraft, themethod including: receiving, at a flight controller, a control datumfrom a input control; and generating, at the flight controller, acommand datum as a function of the control datum and an authoritystatus, wherein the authority status comprises the amount of control auser has over the electric aircraft while the electric aircraft isengaged in an autopilot mode; and initiating an operation of a flightcomponent of the electric aircraft as a function of the command datumand the authority status.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system forremote pilot control of an electric aircraft;

FIG. 2 is an illustrative flow diagram for an exemplary embodiment of amethod for remote pilot control of an electric aircraft;

FIG. 3 is an illustrative diagram of a flight controller;

FIG. 4 is an exemplary diagram of a machine-learning model;

FIG. 5 is an exemplary representation of an electric aircraft; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for a remote pilot control of an electric aircraftduring autopilot mode. In an embodiment, system receives an input from auser, which translates into a control datum, where the control datum issent to a flight controller that compares that control input against aset of limitations and transmits a command datum to actuator of theelectric aircraft in response. Furthermore, information regarding thecommand datum, such as warnings and command instructions for the user,may be transmitted to the computing device, which may display thecommand datum information on a display of the computing device for theuser.

Aspects of the present disclosure may include a system having anautopilot mode that controls an electric aircraft when no user input isreceive or if the user has no authority, as determined by a flightcontroller of the system. Other aspects of the present disclosure mayinclude a flight controller of the system determining the level ofadjustment required by the user input to determine a magnitude ofauthority by a user. Aspects of the present disclosure can also be usedto display instructions to the user related to the magnitude ofauthority of a user. This is so, at least in part, because the flightcontroller determines compliance of the inputs based on authority statusof a remote computing device and/or a flight plan and/or a flightcontrol algorithm.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 forremote pilot control of an electric aircraft is illustrated inaccordance with one or more embodiments of the present disclosure. Inone or more embodiments, system 100 includes a flight controller 104.Flight controller 104 may include any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Flight controller 104 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. Flight controller104 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting flightcontroller 104 to one or more of a variety of networks, and one or moredevices. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus, or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Flightcontroller 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Flight controller 104 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. Flight controller 104 may distribute oneor more computing tasks as described below across a plurality ofcomputing devices of computing device, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. Flight controller 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or a computing device.

With continued reference to FIG. 1 , flight controller 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, flightcontroller 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Flight controller 104 may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 1 , system 100 includes a remote computingdevice 108. Remote computing device 108 may include a computing deviceor plurality of computing devices consistent with the entirety of thisdisclosure. Remote computing device 108 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, remote computing device 108may be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Remote computing device108 may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

In other embodiments, remote computing device 108 may include an inputcontrol such as a throttle lever, inceptor stick, collective pitchcontrol, steering wheel, brake pedals, pedal controls, toggles,joystick. One of ordinary skill in the art, upon reading the entirety ofthis disclosure would appreciate the variety of input controls that maybe present in an electric aircraft consistent with the presentdisclosure. Inceptor stick may be consistent with disclosure of inceptorstick in U.S. patent application Ser. No. 17/001,845 and titled “A HOVERAND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which isincorporated herein by reference in its entirety. Collective pitchcontrol may be consistent with disclosure of collective pitch control inU.S. patent application Ser. No. 16/929,206 and titled “HOVER AND THRUSTCONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporated hereinby reference in its entirety. Additionally, or alternatively, remotecomputing device 108 may include one or more data sources providing rawdata. “Raw data”, for the purposes of this disclosure, is datarepresentative of aircraft information that has not been conditioned,manipulated, or processed in a manner that renders data unrepresentativeof aircraft information.

Continuing to refer to FIG. 1 , remote computing device 108 isconfigured to receive user input 112. User input 112 may include aphysical manipulation of a control like a pilot using a hand and arm topush or pull a lever, or a pilot using a finger to manipulate a switch.Computing device 108 may include buttons, switches, or other binaryinputs in addition to, or alternatively than digital controls aboutwhich a plurality of inputs may be received. User input 112 may includea voice command by a pilot to a microphone and computing systemconsistent with the entirety of this disclosure.

Continuing to refer to FIG. 1 , remote computing device 108 isconfigured to generate a control datum 116 as a function of user input112. “Control datum”, for the purposes of this disclosure, refers to anyelement of data identifying and/or a user input or command. Remotecomputing device 108 may be communicatively connected to any othercomponent presented in system, the communicative connection may includeredundant connections configured to safeguard against single-pointfailure. Control datum 116 may indicate a pilot's desire to change theheading or trim of an electric aircraft. Control datum 116 may indicatea pilot's desire to change an aircraft's pitch, roll, yaw, or throttle.In an embodiment, “Pitch”, for the purposes of this disclosure refers toan aircraft's angle of attack, that is the difference between theaircraft's nose and the horizontal flight trajectory. For example, anaircraft pitches “up” when its nose is angled upward compared tohorizontal flight, like in a climb maneuver. In another example, theaircraft pitches “down”, when its nose is angled downward compared tohorizontal flight, like in a dive maneuver. “Roll” for the purposes ofthis disclosure, refers to an aircraft's position about its longitudinalaxis, that is to say that when an aircraft rotates about its axis fromits tail to its nose, and one side rolls upward, like in a bankingmaneuver. “Yaw”, for the purposes of this disclosure, refers to anaircraft's turn angle, when an aircraft rotates about an imaginaryvertical axis intersecting the center of the earth and the fuselage ofthe aircraft. “Throttle”, for the purposes of this disclosure, refers toan aircraft outputting an amount of thrust from a propulsor. User input112, when referring to throttle, may refer to a pilot's desire toincrease or decrease thrust produced by at least a propulsor. Controldatum 116 may include an electrical signal. Electrical signals mayinclude analog signals, digital signals, periodic or aperiodic signal,step signals, unit impulse signal, unit ramp signal, unit parabolicsignal, signum function, exponential signal, rectangular signal,triangular signal, sinusoidal signal, sinc function, or pulse widthmodulated signal. Remote computing device 108 may include circuitry,computing devices, electronic components, or a combination thereof thattranslates user input 112 into at least an electronic signal, such ascontrol datum 116, configured to be transmitted to another electroniccomponent, such as flight controller 104.

With continued reference to FIG. 1 , flight controller 104 iscommunicatively connected to remote computing device 108 and configuredto receive control datum 116 from remote computing device 108.“Communicatively connected”, for the purposes of this disclosure, is aprocess whereby one device, component, or circuit is able to receivedata from and/or transmit data to another device, component, or circuit;communicative connection may be performed by wired or wirelesselectronic communication, either directly or by way of one or moreintervening devices or components. In an embodiment, communicativeconnection includes electrically connection an output of one device,component, or circuit to an input of another device, component, orcircuit. Communicative connection may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connection may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical connection, or the like.In one or more embodiments, flight controller 104 is configured togenerate command datum 120 as a function of control datum 116 and anauthority status 136. “Command datum” may include any data describing anadjustment to and/or operation of at least a flight component in anelectric aircraft. Command datum 120 may indicate a command to changethe heading or trim of an electric aircraft. Command datum 120 mayfurther include a command to adjust the torque produced by a propulsorin an electric aircraft. Command datum 120 may indicate a command tochange an aircraft's pitch, roll, yaw, or throttle. Command datum 120may be a command that is within flight limits set by a user, such as amaximum speed, minimum altitude, or the like. Command datum 120 may begenerated based on an authority status of control datum set, forexample, by a flight control algorithm. Command datum 120 may include adesired torque, wherein the at least a flight component may beconfigured to operate at the desired torque. Command datum 120 may alsoinclude at least an element of data identifying at least a command, atleast a maneuver, at least an element of the flight control algorithm,at least an element of a flight plan, such as a user input at the inputcontrol required to achieve the flight plan and the like. In one or moreembodiments flight component may initiate an operation of flightcomponent of electric aircraft as a function of the command datum andthe authority status. An operation of a flight component may include anactuator moving flight component as a function of the command datum andauthority status. For example, and without limitation, an operation of aflight component may include an initiating or terminating an operationof a pusher component of an electric aircraft. More specifically, aflight controller may be configured to initiate operation of the pushercomponent which, in an embodiment, includes initiating rotation of thepusher component such that the rotation of the pusher componentgenerates forward or substantially horizontal thrust. The flightcontroller may be configured to terminate operation of a lift componentwhich, in an embodiment, includes terminating rotation of the liftcomponent (for example, by cutting power to it) such that the liftcomponent and/or the aircraft no longer generates upward orsubstantially vertical thrust. Control of a flight component by a flightcontroller may be consistent with methods for flight control in in U.S.patent application Ser. No. 17/383,703 and titled “METHOD FOR FLIGHTCONTROL OF AN ELECTRIC VERTICAL TAKEOFF AND LANDING AIRCRAFT”, which isincorporated herein by reference in its entirety.

In one or more embodiments, an “authority status” is a level ofauthority assigned to a control datum that relates to the amount ofcontrol a user, such as a pilot, and corresponding remote computingdevice has over an electric aircraft in an autopilot mode. In one ormore embodiments, an authority status may include “full” control, wherea user may obtain complete, indefinite control of an electric aircraft.For instance, and without limitation, a flight controller 104 mayreceive control datum 116 from computing device 108 and generate acommand datum 120 that completely follows the instructions of controldatum 116. For example, and without limitation, if control datum 116transmitted by remote computing device 108 includes a substantialadjustment in the current attitude and/or trajectory of electricaircraft 132, then the user may be given full control over electricaircraft 132. Furthermore, if the control datum 116 includes an attitudeand/or trajectory change of electric aircraft 132 substantial enough,autopilot mode may temporarily disengage and allow user and/or remotecomputing device control electric aircraft 132 until flight control 104no longer receives control datum 116 from remote computing device 108 oruntil a user input 112 includes instructing flight controller 104 toreengage autopilot mode. In one or more embodiments, an authority statusmay include “modified” control, where a user has partial control of anelectric aircraft. For example, and without limitation, if a controldatum 116 transmitted by remote computing device 108 includes anattitude and/or trajectory change of electric aircraft 132 that isminor, then user may gain temporary control over electric aircraft 132to execute the command for the minor change before an autopilot featureof the flight controller retains fully autonomous control of theelectric aircraft again via autopilot mode. In one or more embodiments,an authority status may include no control, where the user has nocontrol over an electric aircraft during autopilot mode and thus theflight controller 104 maintains fully-autonomous control over electricaircraft 132. For example, and without limitation, if no control datum116 is received from remote computing device 108, then the autopilotfeature may retain full authority of electric aircraft 132.

In one or more embodiments, flight controller 104 may implement a flightcontrol algorithm to determine an authority status of remote computingdevice 108. For the purposes of this disclosure, a “flight controlalgorithm” is an algorithm that sets associates an authority status witha corresponding command datum. Flight control algorithm may includemachine-learning processes that are used to calculate a set of authoritystatuses and command datum. Machine-learning process may be trained byusing training data associated with past calculations for the electricaircraft, data related to past calculations in other aircrafts,calculations performed based on simulated data, or any other trainingdata described in this disclosure. Authority status may include controlthresholds, where performing commands within of a predetermined rangeresult in full control of electric aircraft 132 by remote computingdevice 108, partial control of electric aircraft 132 by remote computingdevice 108, or no control of electric aircraft 132 by remote computingdevice 108, and thus, full autopilot control. For example, and withoutlimitation, if a control datum 116 is within a low-range threshold, thenauthority status 136 may be determined by fight controller 104 to be nocontrol and autopilot maintains full control of electric aircraft 132.In another example, and without limitation, if control datum 116 iswithin a mid-range threshold, then authority status 136 is determined byflight controller 104 to be partial control and remote computing device108 may receive partial control of electric aircraft 132. In anotherexample, and without limitation, if control datum 116 is within ahigh-range threshold, then authority status 136 may be determined byflight controller 104 to be full control and remote computing device 108may receive complete control of electric aircraft 132.

In an embodiment, flight control algorithm may be received from a remotedevice. In some embodiments, flight control algorithm is generated byflight controller 104. In one or more embodiments, flight controlalgorithm may be generated as a function of a user input. In someembodiments, flight control algorithm may be generated by the flightcontroller as a function of a flight plan. Flight plan may be receivedfrom a plurality of sources such as a user and/or pilot, Air TrafficControl, Fleet operator, third-parties, such as a contractor, and thelike.

Alternatively or additionally, and with continued reference to FIG. 1 ,system 100 may include actuator 128 which is communicatively connectedto flight controller 104. In an embodiment, actuator 128 may becommunicatively connected to the input control. Actuator 128 isconfigured to receive command datum 120 from flight controller 104. Inan embodiment, actuator 128 may be configured to receive control datum116 from input control 108. In an embodiment, input control 108translates user input 112 into control datum 116, where remote computingdevice 108 is configured to translate user input 112, in the form ofmoving an inceptor stick, for example, into electrical signals to atleast actuator 128 that in turn, moves at least a portion of theaircraft in a way that manipulates a fluid medium, like air, toaccomplish the pilot's desired maneuver. In other embodiments, remotecomputing device 108 translates user input 112 into control datum 116 toflight controller 104, where flight controller 104 may then transmitcommand datum 120 according to a determined authority status 136 ofremote computing device 108. Command datum 120 may then be transmittedto actuator 128, which in turn moves a flight component of aircraft 132so that electric aircraft 132 may execute a user's desired input.

Still referring to FIG. 1 , in an embodiment, actuator 128 may include acomputing device or plurality of computing devices consistent with theentirety of this disclosure. Actuator 128 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, flight actuator 128 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Actuator 128 may performany step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Continuing to refer to FIG. 1 , actuator 128 may include a piston andcylinder system configured to utilize hydraulic pressure to extend andretract a piston connected to at least a portion of electric aircraft.Actuator 128 may include a stepper motor or server motor configured toutilize electrical energy into electromagnetic movement of a rotor in astator. Actuator 128 may include a system of gears connected to anelectric motor configured to convert electrical energy into kineticenergy and mechanical movement through a system of gears. Actuator 128may include one or more inverters capable of driving one or morepropulsors consistent with the entirety of this disclosure utilizing theherein disclosed system. Actuator 128, one of the combination ofcomponents thereof, or another component configured to receive data fromflight controller 104 and input control 108, if loss of communication isdetected, may be configured to implement a reduced function controller.The reduced function controller may react directly to input control 108,or other raw data inputs, as described in the entirety of thisdisclosure. Actuator 128 may include components, processors, computingdevices, or the like configured to detect, as a function of time, lossof communication with flight controller 104.

Continuing to refer to FIG. 1 , actuator 128 may be further configuredto command a flight component as a function of command datum 120. In oneor more embodiments, a flight component may include a propulsor and/orcontrol surface. In some embodiments commanding at least a flightcomponent includes changing a movement and/or attitude of electricaircraft 132. A movement and/or attitude change of an electric aircraftmay include a change in an aircraft's pitch, roll, yaw, throttle,torque, heading, trim, or any change that causes the aircraft to performa movement. Actuator 128 may be configured to move control surfaces ofthe aircraft in one or both of its two main modes of locomotion oradjust thrust produced at any of the propulsors. These electronicsignals can be translated to aircraft control surfaces. These controlsurfaces, in conjunction with forces induced by environment andpropulsion systems, are configured to move the aircraft through a fluidmedium, an example of which is air. A “control surface” as describedherein, is any form of a mechanical linkage with a surface area thatinteracts with forces to move an aircraft. A control surface mayinclude, as a non-limiting example, ailerons, flaps, leading edge flaps,rudders, elevators, spoilers, slats, blades, stabilizers, stabilators,airfoils, a combination thereof, or any other mechanical surface areused to control an aircraft in a fluid medium. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious mechanical linkages that may be used as a control surface, asused, and described in this disclosure.

In an embodiment, actuator 128 may be mechanically connected to acontrol surface at a first end and mechanically connected to an aircraftat a second end. As used herein, a person of ordinary skill in the artwould understand “mechanically connected” to mean that at least aportion of a device, component, or circuit is connected to at least aportion of the aircraft via a mechanical connection. Said mechanicalconnection can include, for example, rigid connection, such as beamconnection, bellows connection, bushed pin connection, constantvelocity, split-muff connection, diaphragm connection, disc connection,donut connection, elastic connection, flexible connection, fluidconnection, gear connection, grid connection, hirth joints, hydrodynamicconnection, jaw connection, magnetic connection, Oldham connection,sleeve connection, tapered shaft lock, twin spring connection, rag jointconnection, universal joints, or any combination thereof. In anembodiment, mechanical connection can be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical connection can be used to join two pieces ofrotating electric aircraft components. Control surfaces may each includeany portion of an aircraft that can be moved or adjusted to affectaltitude, airspeed velocity, groundspeed velocity or direction duringflight. For example, control surfaces may include a component used toaffect the aircrafts' roll and pitch which may comprise one or moreailerons, defined herein as hinged surfaces which form part of thetrailing edge of each wing in a fixed wing aircraft, and which may bemoved via mechanical means such as without limitation servomotors,mechanical linkages, or the like, to name a few. As a further example,control surfaces may include a rudder, which may include, withoutlimitation, a segmented rudder. The rudder may function, withoutlimitation, to control yaw of an aircraft. Also, control surfaces mayinclude other flight control surfaces such as propulsors, rotatingflight controls, or any other structural features which can adjust themovement of the aircraft.

With continued reference to FIG. 1 , at least a portion of an electricaircraft may include at least a flight component. At least a flightcomponent may include any component of an electric aircraft. In anembodiment, at least a flight component may include a propulsor and thepropulsor may include a propeller, a blade, or any combination of thetwo. A “propulsor”, as used herein, is a component or device used topropel a craft by exerting force on a fluid medium, which may include agaseous medium such as air or a liquid medium such as water. In anembodiment, when a propulsor twists and pulls air behind it, it will, atthe same time, push an aircraft forward with an equal amount of force.The more air pulled behind an aircraft, the greater the force with whichthe aircraft is pushed forward. Propulsor may include any device orcomponent that consumes electrical power on demand to propel an electricaircraft in a direction or other vehicle while on ground or in-flight.The function of a propeller is to convert rotary motion from an engineor other power source into a swirling slipstream which pushes thepropeller forwards or backwards. The propulsor may include a rotatingpower-driven hub, to which are attached several radial airfoil-sectionblades such that the whole assembly rotates about a longitudinal axis.The blade pitch of the propellers may, for example, be fixed, manuallyvariable to a few set positions, automatically variable (e.g. a“constant-speed” type), or any combination thereof. In an embodiment,propellers for an aircraft are designed to be fixed to their hub at anangle similar to the thread on a screw makes an angle to the shaft; thisangle may be referred to as a pitch or pitch angle which will determinethe speed of the forward movement as the blade rotates.

In an embodiment, a propulsor can include a thrust element which may beintegrated into the propulsor. The thrust element may include, withoutlimitation, a device using moving or rotating foils, such as one or morerotors, an airscrew or propeller, a set of airscrews or propellers suchas contra-rotating propellers, a moving or flapping wing, or the like.Further, a thrust element, for example, can include without limitation amarine propeller or screw, an impeller, a turbine, a pump-jet, a paddleor paddle-based device, or the like.

Still referring to FIG. 1 , remote computing device 108 iscommunicatively connected to flight controller 104, where remotecomputing device 108 is configured to receive the command datum 120 fromflight controller 104 and display command datum 120 for a user to view,such as via a display 124. Computing device may be configured to displayinformation to a user. “Displaying” may include information in graphicalform, tactile feedback form, audio form, or any other method ofdelivering information to a user. In an embodiment, remote computingdevice 108 may display command datum 120 and/or authority status 136 tothe user. In embodiments, remote computing device 108 may includemultiple devices, where each device is configured to display commanddatum 120. In other embodiments, computing device may include multipledevices where some of the devices may display information that isdifferent than information displayed in the other devices. In anembodiment, computing device may include a graphical user interface(GUI) incorporated in the electric aircraft. As described herein, agraphical user interface is a form of user interface that allows usersto interact with flight controller 104 through graphical icons and/orvisual indicators. The user may, without limitation, interact withgraphical user interface through direct manipulation of the graphicalelements. Graphical user interface may be configured to display at leastan element of a flight plan, as described in detail below. As anexample, and without limitation, graphical user interface may bedisplayed on any electronic device, as described herein, such as,without limitation, a computer, tablet, and/or any other visual displaydevice. Display 124 is configured to present, to a user, informationrelated to the flight plan. Display 124 may include a graphical userinterface, multi-function display (MFD), primary display, gauges,graphs, audio cues, visual cues, information on a heads-up display (HUD)or a combination thereof. Display 124 may be disposed in a projection,hologram, or screen within a user's helmet, eyeglasses, contact lens, ora combination thereof. Remote computing device 108 may display thecommand datum 120 and/or authority status 136 in graphical form.Graphical form may include a two-dimensional plot of two variables thatrepresent data received by the controller, such as the control datum andthe related authority status. In one embodiment, computing device mayalso display the user's input in real-time. In embodiments, computingdevice may relay the command datum 120 in audio form.

Now referring to FIG. 2 , an exemplary depiction of a method 200 forremote pilot control of an electric aircraft is illustrated in one ormore embodiments of the present disclosure. Method 200, at step 205,includes receiving, at remote computing device 108, a user input 112. Ina nonlimiting example, user input 112 may be a button clicked by theuser at remote computing device 108. In another nonlimiting example,user input 112 may be a voice command specifying a maneuver.

Still referring to FIG. 2 , method 200, at step 210, includesgenerating, by the input control 108, control datum 116 as a function ofuser input 112. In a nonlimiting example, the control datum 116 may be acommand to increase throttle. In another nonlimiting example, controldatum 116 may be an input to change the pitch angle of the electricaircraft. In an embodiment, control datum 116 may be any input thataffects the functionality of the electric aircraft.

Continuing to refer to FIG. 2 , at step 215, method 200 includesreceiving, at flight controller 104, the control datum 116 from remotecomputing device 108. In a nonlimiting example, flight controller 104and remote computing device108 are communicatively connected.Furthermore, flight controller 104 is communicatively connected toelectric aircraft 132.

With continued reference to FIG. 2 , method 200, at step 220, includesdetermining, at flight controller 104, an authority status 136 ofcontrol datum 116. In a nonlimiting example, flight controller 104 maydetermine which predetermined threshold range control datum 116 iswithin, which may be set by a fleet operator, a user, an algorithm offlight controller, or the like.

With continued reference to FIG. 2 , method 200, at step 225, includesgenerating a command datum 120 as a function of control datum 116 andauthority status 136. In another nonlimiting example, once flightcontroller 104 generates command datum 120 according to authority status136 of control datum 116, related information may be displayed on adisplay 124 of remote computing device 108. One of ordinary skill in theart, upon reading the entirety of this disclosure would appreciate thevariety of information that may be present in a command datum related tothe operation of an electric aircraft consistent with the presentdisclosure.

With continued reference to FIG. 2 , method 200, at step 230, mayfurther include receiving, at an actuator, the command datum 120 fromthe flight controller 104, and commanding, by the actuator, at least aflight component as a function of command datum 120. In a nonlimitingexample, after user pushes a throttle lever, a command datum 120 maycreate a signal to increase power to at least a propeller where theactuator will take that signal and effect movement of the at least apropeller. In another embodiment, commanding at least a flight componentmay include changing a movement of electric aircraft 132, such aschanging a pitch angle.

Continuing to refer to FIG. 2 , method 200, at step 235, method 200includes display, at remote computing device 108, command datum 120. Inone or more embodiments, method may include receiving, by computingdevice 108, command datum 120 from flight controller 104 and,consequently, displaying command datum 120. In a nonlimiting example,remote computing device 108 may include a graphical user interface. Inanother nonlimiting example, remote computing device 108 may display anauthority status 136 of control datum 116. One of ordinary skill in theart, upon reading the entirety of this disclosure would appreciate thevariety of ways that the command datum may be received and displayed,including in tactile feedback or audio form, by a computing deviceconsistent with the present disclosure.

Now referring to FIG. 3 , an exemplary embodiment 300 of a flightcontroller 304 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 304 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 304may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 304 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a signal transformation component 308. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 308 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component308 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 308 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 308 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 308 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 3 , signal transformation component 308 may beconfigured to optimize an intermediate representation 312. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 308 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 308 may optimizeintermediate representation 312 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 308 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 308 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 304. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 308 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a reconfigurable hardware platform 316. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 316 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 3 , reconfigurable hardware platform 316 mayinclude a logic component 320. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 320 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 320 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 320 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 320 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 320 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 312. Logiccomponent 320 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 304. Logiccomponent 320 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 320 may beconfigured to execute the instruction on intermediate representation 312and/or output language. For example, and without limitation, logiccomponent 320 may be configured to execute an addition operation onintermediate representation 312 and/or output language.

In an embodiment, and without limitation, logic component 320 may beconfigured to calculate a flight element 324. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 324 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 324 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 324 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 3 , flight controller 304 may include a chipsetcomponent 328. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 328 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 320 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 328 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 320 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 328 maymanage data flow between logic component 320, memory cache, and a flightcomponent 332. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component332 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component332 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 328 may be configured to communicate witha plurality of flight components as a function of flight element 324.For example, and without limitation, chipset component 328 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 3 , flight controller 304may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 304 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 324. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 304 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 304 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 3 , flight controller 304may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 324 and a pilot signal336 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 336may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 336 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 336may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 336 may include an explicitsignal directing flight controller 304 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 336 may include an implicit signal, wherein flight controller 304detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 336 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 336 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 336 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 336 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal336 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 3 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 304 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 304.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 3 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 304 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 3 , flight controller 304 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor, and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 304. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 304 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 304 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 3 , flight controller 304 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus, or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller304 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 304 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 304 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 3 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 332. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 3 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 304. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 312 and/or output language from logiccomponent 320, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 3 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 3 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 3 , flight controller 304 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 304 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 3 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 3 , flight controller may include asub-controller 340. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 304 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 340may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 340 may include any component of any flightcontroller as described above. Sub-controller 340 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 340may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 340 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 3 , flight controller may include aco-controller 344. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 304 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 344 mayinclude one or more controllers and/or components that are similar toflight controller 304. As a further non-limiting example, co-controller344 may include any controller and/or component that joins flightcontroller 304 to distributer flight controller. As a furthernon-limiting example, co-controller 344 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 304 to distributed flight control system. Co-controller 344may include any component of any flight controller as described above.Co-controller 344 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 3 , flightcontroller 304 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 304 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 408 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 5 , an embodiment of an electric aircraft 500 ispresented. Still referring to FIG. 5 , electric aircraft 500 may includea vertical takeoff and landing aircraft (eVTOL). As used herein, avertical take-off and landing (eVTOL) aircraft is one that can hover,take off, and land vertically. An eVTOL, as used herein, is anelectrically powered aircraft typically using an energy source, of aplurality of energy sources to power the aircraft. In order to optimizethe power and energy necessary to propel the aircraft. eVTOL may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Rotor-basedflight, as described herein, is where the aircraft generated lift andpropulsion by way of one or more powered rotors connected with anengine, such as a “quad copter,” multi-rotor helicopter, or othervehicle that maintains its lift primarily using downward thrustingpropulsors. Fixed-wing flight, as described herein, is where theaircraft is capable of flight using wings and/or foils that generatelife caused by the aircraft's forward airspeed and the shape of thewings and/or foils, such as airplane-style flight.

With continued reference to FIG. 5 , a number of aerodynamic forces mayact upon the electric aircraft 500 during flight. Forces acting on anelectric aircraft 500 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 500 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 500 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 500 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 500 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 500 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 500 downward due to the force of gravity. Anadditional force acting on electric aircraft 500 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor of the electric aircraft. Liftgenerated by the airfoil may depend on speed of airflow, density of air,total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 500 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of an electric aircraft500, including without limitation propulsors and/or propulsionassemblies. In some embodiments, electric aircraft 500 may include atleast on vertical propulsor 504. In an embodiment, electric aircraft 500may include at least one forward propulsor 508. In an embodiment, themotor may eliminate need for many external structural features thatotherwise might be needed to join one component to another component.The motor may also increase energy efficiency by enabling a lowerphysical propulsor profile, reducing drag and/or wind resistance. Thismay also increase durability by lessening the extent to which dragand/or wind resistance add to forces acting on electric aircraft 500and/or propulsors.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for remote pilot control of an electricaircraft during autopilot, the system comprising: a flight controllercommunicatively connected to a remote computing device and an electricaircraft, wherein the flight controller is configured to: receive acontrol datum from an input control; determine an authority status ofthe control datum, wherein determining the authority status furthercomprises: establishing a control threshold of a plurality ofpredetermined control thresholds, wherein the authority status comprisesa level of control, as determined by the established control threshold,a user has over the electric aircraft while the electric aircraft isengaged in an autopilot mode, wherein the plurality of predeterminedcontrol thresholds includes a first level of control of the electricaircraft by the user and a second level of control of the electricaircraft by the user, wherein the second level of control allows theuser greater control of the electric aircraft compared to the firstlevel of control; generate a command datum as a function of the controldatum and the authority status; and initiate an operation of a flightcomponent of the electric aircraft as a function of the command datumand the authority status.
 2. The system of claim 1, further comprisingan actuator connected to the flight controller, wherein the actuator isconfigured to: receive the command datum from the flight controller; andmove the flight component as a function of the command datum and theauthority status.
 3. The system of claim 2, wherein the actuator isconfigured to convert the command datum into mechanical movement of theflight component of the electric aircraft.
 4. The system of claim 1,wherein the authority status comprises full control by the remotecomputing device.
 5. The system of claim 1, wherein the flightcontroller is configured to generate a command datum that is the same asthe control datum.
 6. The system of claim 1, wherein the authoritystatus comprises modified control by the remote computing device, whichincludes temporary control of the electric aircraft by the remotecomputing device.
 7. The system of claim 1, wherein the authority statuscomprises no control so that the flight controller maintains autonomouscontrol of the electric aircraft.
 8. The system of claim 1, wherein theflight controller is configured to generate the command datum as afunction of a user input.
 9. The system of claim 1, wherein the electricaircraft is an electric vertical takeoff and landing aircraft.
 10. Amethod for autopilot in an electric aircraft, the method comprising:receiving, at a flight controller communicatively connected to a remotecomputing device and an electric aircraft, a control datum from an inputcontrol; determining, at the flight controller, an authority status ofthe control datum, wherein determining the authority status furthercomprises: establishing a control threshold of a plurality ofpredetermined control thresholds, wherein the authority status comprisesa level of control, as determined by the established control threshold,a user has over the electric aircraft while the electric aircraft isengaged in an autopilot mode, wherein the plurality of predeterminedcontrol thresholds includes a first level of control of the electricaircraft by the user and a second level of control of the electricaircraft by the user, wherein the second level of control allows theuser greater control of the electric aircraft compared to the firstlevel of control; generating, at the flight controller, a command datumas a function of the control datum and the authority status; andinitiating, at the flight controller, an operation of a flight componentof the electric aircraft as a function of the command datum and theauthority status.
 11. The method of claim 10, further comprising:receiving, by an actuator connected to the flight controller, thecommand datum from the flight controller; and moving, by the actuator,the flight component as a function of the command datum and theauthority status.
 12. The method of claim 10, further comprisingconverting, by the actuator, the command datum into mechanical movementof the flight component of the electric aircraft.
 13. The method ofclaim 10, further comprising: receiving, by the remote computing device,the command datum from the flight controller; and displaying, by theremote computing device, the command datum.
 14. The method of claim 10,wherein the authority status comprises full control by the remotecomputing device, which includes indefinite control of the electricaircraft by the remote computing device.
 15. The method of claim 10,further comprising generating, by the flight controller, a command datumthat is the same as the control datum.
 16. The method of claim 10,wherein the authority status comprises modified control by the remotecomputing device, which includes temporary control of the electricaircraft by the remote computing device.
 17. The method of claim 10,wherein the authority status comprises no control so that the flightcontroller maintains autonomous control of the electric aircraft. 18.The method of claim 10, further comprising generating, by the flightcontroller, the command datum as a function of a user input.
 19. Themethod of claim 10, wherein the electric aircraft is an electricvertical takeoff and landing aircraft.